MetaGPT( The Multi-Agent Framework):颠覆AI开发的革命性多智能体元编程框架
"MetaGPT( The Multi-Agent Framework):颠覆AI开发的革命性多智能体元编程框架"
一个多智能体元编程框架,给定一行需求,它可以返回产品文档、架构设计、任务列表和代码。这个项目提供了一种创新的方式来管理和执行项目,将需求转化为具体的文档和任务列表,使项目管理变得高效而智能。对于需要进行规划和协调的项目,这个框架提供了强大的支持.
MetaGPT's 能力展示
https://github.com/geekan/MetaGPT/assets/34952977/34345016-5d13-489d-b9f9-b82ace413419
例如,如果你输入' python startup.py ' ' Design a RecSys like今日头条' ',你会得到很多输出,其中之一是data & api Design
生成一个分析和设计示例的成本约为0.2美元(在GPT-4 API费用中),而生成一个完整项目的成本约为2.0美元。
1.安装
1.1 安装视频指南
- 常规安装
#Step 1: Ensure that NPM is installed on your system. Then install mermaid-js. (If you don't have npm in your computer, please go to the Node.js offical website to install Node.js https://nodejs.org/ and then you will have npm tool in your computer.)
npm --version
sudo npm install -g @mermaid-js/mermaid-cli
#Step 2: Ensure that Python 3.9+ is installed on your system. You can check this by using:
python --version
#Step 3: Clone the repository to your local machine, and install it.
git clone https://github.com/geekan/metagpt
cd metagpt
pip install -e.
Note:
-
If already have Chrome, Chromium, or MS Edge installed, you can skip downloading Chromium by setting the environment variable
PUPPETEER_SKIP_CHROMIUM_DOWNLOAD
totrue
. -
Some people are having issues installing this tool globally. Installing it locally is an alternative solution,
npm install @mermaid-js/mermaid-cli
-
don't forget to the configuration for mmdc in config.yml
PUPPETEER_CONFIG: "./config/puppeteer-config.json" MMDC: "./node_modules/.bin/mmdc"
-
if
pip install -e.
fails with error[Errno 13] Permission denied: '/usr/local/lib/python3.11/dist-packages/test-easy-install-13129.write-test'
, try instead runningpip install -e. --user
-
将Mermaid图表转换为SVG、PNG和PDF格式。除了Node.js版本的mermaid - cli之外,你现在还可以选择使用Python版本的剧作家、pyppeteer或mermaid。
-
Playwright
- Install Playwright
pip install playwright
- Install the Required Browsers
to support PDF conversion, please install Chrominum.
playwright install --with-deps chromium
- modify
config.yaml
uncomment MERMAID_ENGINE from config.yaml and change it to
playwright
MERMAID_ENGINE: playwright
-
pyppeteer
- Install pyppeteer
pip install pyppeteer
- Use your own Browsers
pyppeteer alow you use installed browsers, please set the following envirment
export PUPPETEER_EXECUTABLE_PATH = /path/to/your/chromium or edge or chrome
please do not use this command to install browser, it is too old
pyppeteer-install
- modify
config.yaml
uncomment MERMAID_ENGINE from config.yaml and change it to
pyppeteer
MERMAID_ENGINE: pyppeteer
-
mermaid.ink
- modify
config.yaml
uncomment MERMAID_ENGINE from config.yaml and change it to
ink
MERMAID_ENGINE: ink
Note: this method does not support pdf export.
- modify
-
1.2 Docker安装指南
#Step 1: Download metagpt official image and prepare config.yaml
docker pull metagpt/metagpt:latest
mkdir -p /opt/metagpt/{config,workspace}
docker run --rm metagpt/metagpt:latest cat /app/metagpt/config/config.yaml > /opt/metagpt/config/key.yaml
vim /opt/metagpt/config/key.yaml # Change the config
#Step 2: Run metagpt demo with container
docker run --rm \
--privileged \
-v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \
-v /opt/metagpt/workspace:/app/metagpt/workspace \
metagpt/metagpt:latest \
python startup.py "Write a cli snake game"
#You can also start a container and execute commands in it
docker run --name metagpt -d \
--privileged \
-v /opt/metagpt/config/key.yaml:/app/metagpt/config/key.yaml \
-v /opt/metagpt/workspace:/app/metagpt/workspace \
metagpt/metagpt:latest
docker exec -it metagpt /bin/bash
$ python startup.py "Write a cli snake game"
The command docker run ...
do the following things:
- Run in privileged mode to have permission to run the browser
- Map host directory
/opt/metagpt/config
to container directory/app/metagpt/config
- Map host directory
/opt/metagpt/workspace
to container directory/app/metagpt/workspace
- Execute the demo command
python startup.py "Write a cli snake game"
1.3 构造自定义
#You can also build metagpt image by yourself.
git clone https://github.com/geekan/MetaGPT.git
cd MetaGPT && docker build -t metagpt:custom .
1.4 相关配置
- Configure your
OPENAI_API_KEY
in any ofconfig/key.yaml / config/config.yaml / env
- Priority order:
config/key.yaml > config/config.yaml > env
#Copy the configuration file and make the necessary modifications.
cp config/config.yaml config/key.yaml
Variable Name | config/key.yaml | env |
---|---|---|
OPENAI_API_KEY # Replace with your own key | OPENAI_API_KEY: "sk-..." | export OPENAI_API_KEY="sk-..." |
OPENAI_API_BASE # Optional | OPENAI_API_BASE: "https://<YOUR_SITE>/v1" | export OPENAI_API_BASE="https://<YOUR_SITE>/v1" |
2.相关教程
#Run the script
python startup.py "Write a cli snake game"
#Do not hire an engineer to implement the project
python startup.py "Write a cli snake game" --implement False
#Hire an engineer and perform code reviews
python startup.py "Write a cli snake game" --code_review True
After running the script, you can find your new project in the workspace/
directory.
- Preference of Platform or Tool
You can tell which platform or tool you want to use when stating your requirements.
python startup.py "Write a cli snake game based on pygame"
- 使用指南
NAME
startup.py - We are a software startup comprised of AI. By investing in us, you are empowering a future filled with limitless possibilities.
SYNOPSIS
startup.py IDEA <flags>
DESCRIPTION
We are a software startup comprised of AI. By investing in us, you are empowering a future filled with limitless possibilities.
POSITIONAL ARGUMENTS
IDEA
Type: str
Your innovative idea, such as "Creating a snake game."
FLAGS
--investment=INVESTMENT
Type: float
Default: 3.0
As an investor, you have the opportunity to contribute a certain dollar amount to this AI company.
--n_round=N_ROUND
Type: int
Default: 5
NOTES
You can also use flags syntax for POSITIONAL ARGUMENTS
2.1 快速开始
It is difficult to install and configure the local environment for some users. The following tutorials will allow you to quickly experience the charm of MetaGPT.
Try it on Huggingface Space
- 相关链接
https://github.com/geekan/MetaGPT/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d
3.更多推荐:终端LLM AI模型:mlc-llm
大型语言模型的机器学习编译(MLC LLM)是一种高性能的通用部署解决方案,允许使用带有编译器加速的本机api来本地部署任何大型语言模型。这个项目的使命是让每个人都能使用ML编译技术在每个人的设备上开发、优化和部署人工智能模型。
项目链接:https://github.com/mlc-ai/mlc-llm
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